The present embodiments relate to medical diagnostic ultrasound imaging. In particular, fetal measurements are performed using ultrasound data representing a volume.
Fetal biometric measurements represent an important factor for high quality obstetrics health care. Fetal biometric measurements are used for estimating the gestational age (GA) of the fetus, assessing of fetal size, and monitoring of fetal growth and health. To perform the measurements, a physician or sonographer manually searches for a standardized plane using 2-D ultrasound (2DUS) images. Manual searching is cumbersome and contributes to excessive length in clinical obstetric examinations. Long ultrasound examinations may lead to increased costs.
Three-dimensional ultrasound (3DUS) data may have increasing importance in radiology for fetal diagnosis. Compared to 2DUS, the main advantages of 3D US may include a substantial decrease in the examination time, a possibility of post-exam data processing without requesting additional visits of the patient, and the ability of experts to produce 2-D views of the fetal anatomies in orientations that cannot be seen in common 2-D ultrasound exams. However, extensive manipulation on the part of the physician or the sonographer may be required in order to identify standard planes for measurements from the 3DUS data. The learning curve to understand these manipulation steps is quite large, even for expert users. Usually, expert users find several landmarks in order to reach the sought anatomy. For example, the standardized plane for measuring the lateral ventricles in the fetus brain is referred to as the transventricular plane. The user searches for the cavum septi pellucidi, frontal horn, atrium, and choroids plexus in order to identify the plane. Since the fetus is oriented in an arbitrary position in each volume, an expert sonographer may require several minutes to localize the structures in a basic examination.
Some automated or semi-automated processes may be used to assist in 3DUS. In the field of 3DUS, segmentation and registration of specific anatomical structures may be provided. However, segmentation and registration merely separate data representing an already identified structure.
In computer vision, there are methods for recognizing 3D objects using range images, but these applications are different in the sense that the system works with surfaces instead of actual volumes. Using 3-D magnetic resonance imaging (3DMRI) data, a combination of a discriminant classifier based on the probabilistic boosting tree (PBD) for appearance and generative classifier based on principal components analysis (PCA) for shape, where the weights for these two terms are learned automatically, has been proposed. This is applied to the segmentation of eight brain structures, where the system takes eight minutes to run. Segmentation of heart structures using 3-D computed tomography (CT) may be based on discriminant classifiers and marginal space learning. The segmentation of four heart structures may be achieved in less than eight seconds. However, 3DUS data is different than CT or MRI data. The orientation of anatomical structures in MRI and CT data is generally better constrained than that of 3DUS.